MODEL-CENTRIC DESIGN AND DEVELOPMENT FOR ECO-FRIENDLY SHIPS
Korea Maritime Week, 27 June 2018
Kevin Koosup Yum
Content
• SINTEF Ocean
• Model-Centric Design, Why?
• Main concepts for Model-Centric Design, What and How?
• Relataed Researches
• Conclusion
2
Our role
Innovation
Develop new technology and knowledge
Contract research
R&D-partner to industry and government
Laboratories and software
Testing, development and verification
Sustainable development
Deliver environmentally friendly solutions
New ventures
Create new products and spin-offs
Social mission
Knowledge to social debate and politics
Aquaculture
Fisheries
Processing industry Maritime
New marine resources
Offshore wind Oil and gas
SubseaEnvironmental
technology
Ocean laboratory
5
World leading laboratories
Robot laboratory
Towing tank
Full scale aquaculture site Construction labFlume tank
Plankton centre Oil laboratory
Model-Centric Design, Why? – Challenges ahead
• Disruptive changes in the industries
• Digitalization
• Autonomous shipping
• Strenthening emission regulations
• EEDI / EEOI
• NOx regulations
• Global Sulfurcap
• Radical green house gas reduction
(MEPC 72, 40% by 2035 & 70% by 2050, per transport work)
6
Smith et al. (2016)
Options for CO2 reduction
7
Ves
sels
ize
Hu
ll sh
ape
LW m
ater
ials
Air
lub
rica
tio
n
Res
ist.
red
. dev
ice
Bal
last
wat
er r
edu
ctio
n
Hu
ll co
atin
g
Hyb
rid
po
wer
/pro
pu
lsio
n
Pow
er s
yste
m/m
ach
iner
y
Pro
p. e
ffic
ien
cyd
evic
es
Was
te h
eat
reco
very
On
bo
ard
po
wer
dem
and
Bio
fuel
s
LNG
Win
d p
ow
er
Fuel
cells
Co
ld ir
on
ing
Sola
r p
ow
er
Spee
d o
pti
miz
atio
n
Cap
acit
yu
tiliz
atio
n
Vo
yage
op
tim
izat
ion
Oth
ero
per
atio
nal
mea
s.
Hull design
Power / PropulsionSystem
Fuel
AlternativeEnergy source
Operation
Bouman et al. (2017)
Model-Centric Design, Why? - Real Challenges
• Complexity
• Combinations of the options
• Operational profiles: mode, route and weather, speed
• Market Scenarios
8Erikstad et al. (2015)
Model-Centric Design, Why?
• Testing and Verification Challenges
9
10 Vessel Hull Variants
10 Propulsion/Power System Variants
5 Automation / Control System Variants
500 Operational Test Cases
15 Parameters
100 cases
500 cases
250 000 cases
3 750 000 cases x 0.5 hours
1 875 000 hours
MAIN CONCEPTS
10
Model-Centric Design, What is it?
MCD
Coupling ofSystem
Behavior and Structure
Verification and Validation
System Architecture
Tools for Analysis and Decision Support
• Design Thinking and Systems Perspective Analysis
Development with X-in-the-Loop Testing
13 https://www.iem.fraunhofer.de/en/kompetenzen/unsereforschungsabteilungen/regelungstechnik/leistungsangebot/X-in-the-LoopEntwicklungs-undTestumgebungen.html
http://www.acosar.eu/overview.php
Co-simulation
• Simulation of heterogeneous systems
• Partitioning and parallelization of large systems
• Multirate integration
• Hardware-in-the-loop simulation
14
• Hybrid Testing • Standard Interface
15
Real-time Testing with Hardware
Total System
Numerical simultion
Physical Model
InterfaceActuator
http://www.acosar.eu/overview.php
RELATED RESEARCH ACTIVITIES IN SINTEFOCEAN
16
Smart Maritime - Center of Research Based Innovation
• Main goals
• Strengthen the competitiveness of the
Norwegian maritime industry
• Improve energy efficiency and reduce
emissions
• In brief
• 17 Industry Partners
• 30 Research Scientists / 10 Laboratories
• Budget: 24 MNOK / year (~3 MUSD)
• Period: 2015 – 2023
• Hosted by SINTEF Ocean
Open Simulation Platform
J I P
Open Ship Simulator Platform Project
19
Standardized co-simulation interface – FMI
OEM A OEM B OEM C OEM D
Co-simulation
Master Algorithm
Monitoring
& Control
system
HW SW
Models
I/O
Visualization
Test tools
Scenario
management
Automated testing
Dig
ital Tw
in
com
ponents
Sensor
data
Decentralized
Remote
interface
HIL
setu
p
Longterm Ship Performance Simulator - GYMIR
Input => Hull and propulsion informationPerformance analysis in calm sea and seaway by ShipX
Simulation set up• Routes• Period• Metocean data• Speed policy
Result• Speed• Power • Propeller speed• Fuel consumption
55K Bulk Carrier Case
21
Sail start: 2011-02-01Sail end: 2014-02-01Metocean data : 2011 ~ 2014
Speed policy: 16kts target speed, limited by power
Result
22
Result
23
Case studies – Hybrid propulsion system for a VLCC
25
12
14
0
5
10
15
20
0 1 2 3 4 5 Ves
sel s
pee
d
Per
cen
tage
of
op
erat
ion
al t
ime
Significant wave height
Operational profile
12 13 14
26
Power system modeling
Data analysis
Surrogate modeling
Optimization
Design of experiments
Verification
Machine learning
Case studies – Hybrid propulsion system for a VLCC
Design of ExperimentDynamic Performance SimulationSurrogate ModelingOptimizationVerification of the result
ConfigurationConstraintsObjectives
Models
Case studies – Hybrid propulsion system for a VLCC
Speed [kts] Frequency Speed
9 15%11 50%13 20%15 15%
Hs Scenario 1
Scenario 2
Scenario 3
0 m 5% 5% 5%1 m 10% 10% 12%2 m 10% 20% 45%3 m 55% 55% 28%4 m 20% 10% 10%
Base[kg/m]
Optimum [kg/m]
PME
[MW]PPTI
[MW]PGen
[MW]PBatt
[MW]
1 0.16410.1627
(↓0.85%)24.93 1.884 1.802 1.079
2 0.15000.1492
(↓0.5%)24.41 2.375 1.641 1.266
3 0.13520.1339
(↓0.96%)23.93 3.554 1.239 2.606
Real-Time Hybrid Testing of A Marine Power Plant
28
Numerical simulation (real-time)
Hardware Model
I/O Interface
Actuators
Control System
ReaTHM®
Hybrid Power Laboratory
29
• Application
• Testing and Verification of Power and Energy
Management System (Hybrid DC)
• Prototyping of controllers for the power system
• Real-time Hybrid Testing (ReaTHM) for a marine power
system
• X-in-the-Loop simulation and testing
• Future development
• Marine fuel-cell test bed
• Regenerative braking on the shaft
Test set up
30
Simulation and Development Platform
Operator Interface
Data acquisition and processing
Actuator Control and interface
Model-in-the-loop
Actuator Study
Open / Closed Loop Hybrid Testing with Models
Conclusion
• Challenges ahead are immense in terms of its complexity and
uncertainty.
• Design thinking and system perspective are crucial to overcome the
challenges.
• X-in-the-Loop platform will be a central tool for the development of
new vessels.
31
QUESTIONS?
32
Reference
• Smith, T. W. P., Raucci, C., Haji Hosseinloo, S., Rojon, I., Calleya, J., Suarez de la Fuente, S., Wu, P., Palmer,
K. (2016). CO2 Emissions from International Shipping: Possible reduction targets and their associated
pathways.
• Bouman, E. A., Lindstad, E., Rialland, A. I., & Strømman, A. H. (2017). State-of-the-art technologies ,
measures , and potential for reducing GHG emissions from shipping – A review. Transportation Research
Part D, 52, 408–421.
• Erikstad, S. O., Rehn, C. F. (2015). Handling Uncertainty in Marine Systems Design-State-of-the-Art and
Need for Research, IMDC 2015
• https://www.iem.fraunhofer.de/en/kompetenzen/unsereforschungsabteilungen/regelungstechnik/leistu
ngsangebot/X-in-the-LoopEntwicklungs-undTestumgebungen.html accessed on 4 June
• http://www.acosar.eu/overview.php accessed on 4 June
Teknologi for et bedre samfunn
Top Related